One of the stats that has actually made quite a dent in traditional baseball statistics is OPS (on-base % plus slugging %). OPS is quite useful but, as it turns out, it also leaves a lot to be desired. The main reason for that is the statistic gives equal weights to on-base percentage and slugging percentage when it shouldn’t, and furthermore, both of these statistics themselves are not without their flaws. While OBP does tell you the rate at which a player reaches base, it fails to describe how far the player reached. SLG tells you how many bases a player gained, but unfortunately, it fails to see a walk, hit by pitch, etc as a positive outcome. Also, OBP is actually more valuable than SLG. But the buck doesn’t just stop there. Slugging assumes that each base is worth 1.000, so a single is 1.000, a double 2.000 and so forth which is highly inaccurate. Well, a bunch of smart people (Tom Tango and friends) actually analyzed the true “worth” of each of these outcomes and as it turns out it’s just not that simple. One thing we know for sure is that the worst thing a hitter can do is make an out. After running all of the data, and assuming that an out is worth 0, the actual run values are as follows:

HR =1.70, 3B = 1.37, 2B = 1.08, 1B = 0.77, NIBB = 0.62

So the creator of the formula used these values and applied them to each player. When they did it with a league average player and divided by plate appearances, they got around .300. Pretty nice. But, they realized if they simply multiplied by 15% they got the league average OBP…even better. The statistic is now scaled to OBP so league average is somewhere between .330 and .340 (like OBP). The new values and statistic is now:

(0.72xNIBB + 0.75xHBP + 0.90x1B + 0.92xRBOE + 1.24x2B + 1.56x3B + 1.95xHR) / PA

**NIBB = Non-Intentional Walk, HBP = Hit by Pitch, 1B = Single, RBOE = Reached base on error, 2B = double, 3B = triple, HR = Homerun and PA = Plate Appearance*

Some might be wondering, why is someone rewarded for reached base on error? Well, because it’s actually possible to create errors, kind of. For example, if Bengie Molina hits the ball on the ground anywhere it’s an automatic out if the fielder can get to it. On the other hand, if Chone Figgins hits the ball directly at the short stop and he bobbles it, Figgins may reach on an error. His batting average isn’t rewarded for this in anyway but he has helped his team and in a way Molina never could hope to. And as it turns out, this outcome is actually worth more than a single. Go figure.

Continuing my bashing of Bengie Molina, allow me to show you how his terrible OBP can be quite detrimental. Bengie Molina posted a .727 OPS in 2009, which isn’t very good. Ryan Theriot managed to post an even lower OPS of .712 in 2009. He must be the inferior offensive player. Wrong. Molina’s wOBA is actually .308 to Theriot’s .318. Though Theriot slugged 73 points less than Molina, his OBP was 58 points higher, and, wOBA shows us that his 58 OBP points to Molina’s 73 slugging points were actually worth an additional 10 points in wOBA. This is just a quick example and a good way to illustrate just how much Molina’s extraordinary out making skills truly do hurt his team, offensively of course.

Why am I telling you this? Well, if you like to read my posts here at PaapFly, you better get used to wOBA because I’ll probably start implementing it into them more often. My goal is to not only expand my knowledge of sabermetrics and baseball statistical analysis, but also each of yours. Lastly, I don’t dislike Molina nearly as much as it may seem. He’s actually a somewhat useful player in that he’s neither a terrible hitting catcher nor a terrible defensive catcher. He’s likeable, he’s durable and he plays hard. What’s not to like? Well, Molina simply represents the wrong direction that Brian Sabean has been pointing the Giants’ franchise and, because of this, Molina gets a large portion of my criticism. Sorry, Bengie.

Cheers,

Rory

If you want to read more on wOBA, check this and this out.